Bioinformatics. 2025 Jul 1;41(7):btaf382. doi: 10.1093/bioinformatics/btaf382.

ABSTRACT

MOTIVATION: Rare diseases affect over 300 million people worldwide and are often caused by genetic variants. While variant detection has become cost-effective, interpreting these variants-particularly collecting literature-based evidence like ACMG/AMP PM3-remains complex and time-consuming.

RESULTS: We present AutoPM3, a method that automates PM3 evidence extraction from literatures using open-source large language models (LLMs). AutoPM3 combines a Text2SQL-based variant extractor and a retrieval-augmented generation (RAG) module, enhanced by a variant-specific retriever and fine-tuned LLM, to separately process tables and text. We curated PM3-Bench, a dataset of 1027 variant-publication evidence pairs from ClinGen. On openly accessible pairs, AutoPM3 achieved 86.1% accuracy for variant hits and 72.5% recall for in trans variants-outperforming other methods, including those using larger models. We uncovered the effectiveness of AutoPM3's key modules, especially for variant-specific retriever and Text2SQL, through the sequential ablation study. AutoPM3 located evidence in 76 s, demonstrating that open-source LLMs can offer an efficient, cost-effective solution for rare disease diagnosis.

AVAILABILITY AND IMPLEMENTATION: AutoPM3 is implemented and freely available under the MIT license at https://github.com/HKU-BAL/AutoPM3.

PMID:40586923 | DOI:10.1093/bioinformatics/btaf382

PLoS One. 2025 Jun 30;20(6):e0326372. doi: 10.1371/journal.pone.0326372. eCollection 2025.

ABSTRACT

BACKGROUND: Rare diseases often present with a variety of clinical symptoms and therefore are challenging to diagnose. Fabry disease is an x-linked rare metabolic disorder. The severity of symptoms is usually different in men and women. Since therapeutic options for Fabry disease exist, early diagnosis is important. An artificial intelligence (AI)-based diagnosis support algorithm for rare diseases has been developed in preliminary studies.

OBJECTIVE: Our aim was to extend and train the questionnaire-based AI, capable of distinguishing patients with from those without rare diseases, to achieve satisfactory sensitivity for the detection of a single rare disease, Fabry disease, taking into account gender differences in disease perception.

METHODS: We collected 33 complete datasets from patients with confirmed Fabry disease. These records contained answered AI questionnaires, general information on disease progression, demographic information and quality of life (QoL) measures. The AI was trained to distinguish patients with Fabry disease from patients with relevant differential diagnoses. Its performance was assayed using stratified eleven-fold cross-validation and ROC curve calculation. Variables influencing the performance of the AI were examined with linear regression and calculation of the coefficient of determination.

RESULT: We were able to show that a relatively small sample is sufficient to achieve a sensitivity of 88.12% for the presence of Fabry disease, taking into account gender-specific differences in the disease perception during the pre-diagnostic phase. No confounders of the tool's performance could be found in the data collected concerning the patients' quality of life and diagnostic history.

CONCLUSION: This study illustrates on the example of Fabry disease that differences between female and male Fabry patients, not only in the expression of symptoms, but also with regard to disease perception, might be relevant influencing variables for improving the performance of AI-based diagnostic support tools for rare diseases.

PMID:40587431 | DOI:10.1371/journal.pone.0326372

Nat Biomed Eng. 2025 Jun 27. doi: 10.1038/s41551-025-01429-1. Online ahead of print.

ABSTRACT

RNA engineering has yielded a new class of medicines but faces limitations depending on RNA size and function. Here we demonstrate the synthesis and enzymatic stabilization of telomerase RNA component (TERC), a therapeutically relevant long non-coding RNA (lncRNA) that extends telomere length and replicative lifespan in human stem cells. Compared with therapeutic mRNAs, engineered TERC RNA (eTERC) depends on avoiding nucleoside base modifications and incorporates a distinct trimethylguanosine 5' cap during in vitro transcription. We show that the non-canonical polymerase TENT4B can be repurposed to enzymatically stabilize synthetic RNAs of any size by catalysing self-limited 2'-O-methyladenosine tailing, which is critical for optimal eTERC function in cells. A single transient exposure to eTERC forestalls telomere-induced senescence in telomerase-deficient human cell lines and lengthens telomeres in induced pluripotent stem cells from nine patients carrying different mutations in telomere-maintenance genes, as well as primary CD34+ blood stem/progenitor cells. Our results provide methods and proof of functional reconstitution for a stabilized, synthetic human lncRNA. eTERC may have therapeutic potential to safely extend replicative capacity in human stem cells.

PMID:40579489 | DOI:10.1038/s41551-025-01429-1

Genes (Basel). 2025 May 22;16(6):614. doi: 10.3390/genes16060614.

ABSTRACT

Background/Objectives: Rare diseases are predominantly genetic in etiology and characterized by a prolonged 'diagnostic odyssey'. Advances in genetic testing (GT) have helped shorten the time to diagnosis for rare/undiagnosed conditions. We aimed to synthesize the evidence on psychosocial factors related to GT for rare diseases to inform more person-centered approaches to care. Methods: We conducted a systematic literature search in six databases using structured terms (September 2024). Retrieved articles underwent independent dual review. Data were extracted and collated in tables for analysis. Thematic analysis was used to identify promoters/barriers to GT for patients and families. Findings were validated by a patient advocate and were reported using PRISMA-ScR guidelines. Synthesized findings were mapped to the Theory of Planned Behavior to inform intervention development. Results: Of 1730 retrieved articles, 32 were included for data extraction/synthesis. Studies employed qualitative (n = 19), quantitative (n = 10), and mixed-methods (n = 3) approaches. Nearly all (29/32, 91%) were non-interventional, reporting on decision-making cognitions/processes (19/32, 59%), attitudes/preferences (15/32, 47%), psychosocial impact (6/32, 19%), and knowledge/awareness (4/32, 8%) of pre-conception/prenatal/diagnostic GT and carrier screening. Promoters included understanding GT, ending the diagnostic odyssey, actionable outcomes, personal/family history, altruism, and reproductive decision-making. Barriers included logistical (e.g., distance, cost), psychological burden, perceived lack of benefit, and discrimination/social stigma concerns. Conclusions: Some psychosocial factors related to GT for rare diseases overlap with those in literature on GT for common conditions. Identified factors represent targets for theory-informed, person-centered interventions to support high-quality GT decisions that are informed and aligned with patient/family values and preferences.

PMID:40565506 | DOI:10.3390/genes16060614

Int J Med Inform. 2025 Nov;203:106021. doi: 10.1016/j.ijmedinf.2025.106021. Epub 2025 Jun 21.

ABSTRACT

BACKGROUND: Patients with rare diseases frequently experience misdiagnoses and long diagnostic delays. Accelerating their diagnosis is essential to ensure timely access to appropriate care. Given the increasing availability of EHRs, combining artificial intelligence and deep phenotyping from large-scale clinical databases offers a promising approach to identify undiagnosed patients. This study assesses the impact of improved phenotype extraction on a screening algorithm for Jeune syndrome, a rare ciliopathy characterized by skeletal abnormalities.

METHODS: Phenotypes from Jeune syndrome patients and controls were automatically extracted from patient unstructured EHRs relying on two thesauri separately: the standard UMLS Metathesaurus and the UMLS+, an enhanced version incorporating additional terms identified through deep learning. The machine learning pipeline that we designed for classifying patients with renal ciliopathy was adapted for Jeune syndrome detection. The model was trained and tested on both the datasets created using the two phenotyping strategies.

RESULTS: Using UMLS+ strongly improved the classification of patients with Jeune syndrome, increasing the sensitivity from 49 % to 95 % while maintaining a 90 % specificity. The review of a subset of misclassified controls showed that most of them (69 %) had other genetic skeletal disorders, indicating that the model also captured patients who would benefit from referral to a bone disease geneticist.

CONCLUSION: AI-based screening combined with high-quality deep phenotyping can help reduce diagnostic delay in rare diseases. The completeness and accuracy of phenotyping from EHRs have a strong impact on screening performances.

PMID:40561686 | DOI:10.1016/j.ijmedinf.2025.106021

Med Sci (Basel). 2025 Jun 9;13(2):75. doi: 10.3390/medsci13020075.

ABSTRACT

Background: Next-generation sequencing (NGS), particularly whole-exome sequencing (WES), has become a powerful diagnostic tool for rare genetic conditions. However, its success rate varies based on the underlying genetic etiology and the population studied. Methods: This retrospective study evaluated the diagnostic yield of NGS in a cohort of 137 pediatric patients with suspected rare genetic disorders in Bulgaria, a setting where such testing is not reimbursed and must be self-funded. The patients underwent either WES or targeted gene panel testing based on clinical presentation, family history, and genetic evaluation. Results: The overall diagnostic yield was 45.99%, with WES achieving 51.25% and targeted testing achieving 38.60%. The highest yield was observed in patients presenting with both dysmorphic features and neurodevelopmental delays (62.5%), while the lowest was observed among those with isolated neurodevelopmental issues (10%). A significant portion of the identified variants (35.9%) were novel. Eight patients were diagnosed with copy number variants (CNVs) detected only through WES. Conclusions: Our findings illustrate the value of WES as a first-line test and highlight the impact of deep phenotyping on diagnostic success. This study also emphasizes the need for a population-specific reference genome and equal access to genomic diagnostics in all European countries.

PMID:40559233 | DOI:10.3390/medsci13020075

Front Integr Neurosci. 2025 Jun 9;19:1574758. doi: 10.3389/fnint.2025.1574758. eCollection 2025.

ABSTRACT

There is increasing interest in the utility of electrophysiological measures such as resting EEG and evoked potential (EPs) to serve as biomarkers to facilitate therapeutic development for rare genetic neurodevelopmental disorders (NDDs). Research on this topic thus far has been encouraging, but has also revealed the necessity for unique methods when acquiring EEG and EPs in children with genetic NDDs. Details of these methods are typically beyond the scope of research publications, yet are crucial to the quality and ultimately, usability of the data. In the current manuscript, we detail the methods that we have developed for acquiring EEG and EPs as part of multi-site studies with participants with Rett syndrome, CDKL5 deficiency disorder, MECP2 duplication syndrome, and FOXG1 syndrome. By making our methods accessible, we hope to support other groups interested in acquiring EEG and/or EPs as part of clinical trials or research studies with individuals with genetic NDDs, including groups without prior experience with EEG/EP acquisition. The paper is presented as step-by-step procedures followed by a discussion of issues that may arise during acquisition and ways to troubleshoot these issues. We then discuss considerations for choosing EEG equipment and study paradigms and briefly, considerations for data analysis.

PMID:40552096 | PMC:PMC12183233 | DOI:10.3389/fnint.2025.1574758

Med Anthropol. 2025;44(5):473-487. doi: 10.1080/01459740.2025.2521749. Epub 2025 Jun 23.

ABSTRACT

Public representations of rare diseases often depict patients as neglected and isolated. In response, initiatives promoting patient involvement have emerged, with illness narratives considered as key tools. However, the relationship between narratives and involvement remains underexplored. Based on ethnographic research conducted in Piedmont (Italy), I explore the narratives of two patients, which are deeply entangled with spiritual and religious perspectives, and explore the forms of involvement that emerged within the clinical space. I suggest that, depending on how moral and structural conditions intertwine, patients may be differently legitimized: roles of "experts of experience" or "implicated actors" arose within the field.

PMID:40549659 | DOI:10.1080/01459740.2025.2521749

Hosp Pract (1995). 2025 Feb;53(1):2520743. doi: 10.1080/21548331.2025.2520743. Epub 2025 Jun 22.

ABSTRACT

BACKGROUND: Porphyria is an orphan disease classified as a genetic disorder caused by a partial or high-grade deficiency of enzymes involved in the synthesis of heme, an essential component of hemoglobin. This deficiency results in the accumulation of porphyrins (ALAS1 and PBG), intermediates in the heme metabolic pathway. This accumulation triggers porphyria attacks. In Colombia the Heme Arginate and Human Hematin are the therapeutics alternatives for the management of porphyria Attacks.

OBJECTIVE: To evaluate the comparative effectiveness of heme arginate versus human hemin for treating porphyria attacks in hospitalized patients across three institutions in Medellin, Colombia.

METHODS: An observational and analytical study was conducted to compare the outcomes of treatment with human hematin versus heme arginate in clinical episodes of patients diagnosed with porphyria between 2015-2021.

RESULTS: In episodes treated with heme arginate (ArgH), 75% achieved pain control or reduction, 41.6% showed a reduction in opioid dosage, and 88.8% achieved resolution of the Porphyria attack. For episodes treated with human hematin (HH), 85.3% achieved pain control or reduction, 53.6% showed a reduction in opioid dosage, and 90.2% achieved resolution of the attack. When evaluating the effectiveness of both treatments, no statistically significant differences were observed across the three predefined effectiveness outcomes of the study.

CONCLUSIONS: This study provides a comparative evaluation of heme arginate (ArgH) and human hematin (HH) in the management of Porphyria attacks, demonstrating that both treatments are similarly effective in achieving pain control, reducing opioid use, and resolving clinical attacks.

PMID:40545748 | DOI:10.1080/21548331.2025.2520743

Skelet Muscle. 2025 Jun 20;15(1):17. doi: 10.1186/s13395-025-00382-6.

ABSTRACT

BACKGROUND: Dystrophin-deficient zebrafish larvae are a small, genetically tractable vertebrate model of Duchenne muscular dystrophy that is well suited for early-stage therapeutic development. However, current approaches for evaluating their mobility, a physiologically relevant therapeutic outcome, yield data of low resolution and high variability that provides minimal insight into potential mechanisms responsible for their abnormal locomotion.

METHODS: To address these issues, we used high speed videography and deep learning-based markerless motion capture to quantify escape response (ER) swimming kinematics of two dystrophic zebrafish strains (sapje and sapje-like). Each ER was partitioned into an initiating C-start, a subsequent power stroke, and a final burst of undulatory swimming activity.

RESULTS: Markerless motion capture provided repeatable, high precision estimates of swimming kinematics. Random forest and support vector machine prediction models identified overall ER distance and peak speed, the instantaneous speed conferred by the power stroke, and the average speed and distance covered during burst swimming as the most predictive biomarkers for differentiating dystrophic from wild-type larvae. For each of these predictors, mutant and wild-type larvae differed markedly with effect sizes ranging from 2.4 to 3.7 standard deviations. To identify mechanisms underlying these performance deficits, we evaluated the amplitude and frequency of propulsive tail movements. There was little evidence that tail stroke amplitude was affected by the absence of dystrophin. Instead, temporal aspects of tail kinematics, including tail maximal angular velocity during the C-start and power stroke and tail stroke frequency during burst swimming, were slowed in mutants. In fact, tail kinematics were as effective as direct, non-survival in vitro assessments of tail muscle contractility in differentiating mutant from wild-type larvae.

CONCLUSIONS: ER kinematics can be used as precise and physiologically relevant biomarkers of the dystrophic phenotype, may serve as non-lethal proxies for skeletal muscle dysfunction, and reveal new insights into why mobility is impaired in the absence of dystrophin. The approach outlined here opens new possibilities for the design and interpretation of studies using zebrafish to model movement disorders.

PMID:40542412 | DOI:10.1186/s13395-025-00382-6

HGG Adv. 2025 Jul 10;6(3):100469. doi: 10.1016/j.xhgg.2025.100469. Epub 2025 Jun 16.

ABSTRACT

The total burden of rare diseases is significant worldwide, with over 300 million people being affected. Many rare diseases have both well-defined clinical phenotypes and established genetic causes. However, a remarkable proportion of patients with high clinical suspicion of a rare disease remain genetically undiagnosed and stuck in the diagnostic odyssey after having a cascade of conventional genetic tests. One of the major factors contributing to this is that many types of variants are technically intractable to whole-exome sequencing (WES). In this study, the added diagnostic power of whole-genome sequencing (WGS) for patients with clinically suspected rare diseases was assessed by detecting technically challenging variants. 3,169 patients from the Hong Kong Genome Project (HKGP) were reviewed, identifying 322 individuals having high clinical suspicion of a rare disorder with well-established genetic etiology. Notably, 180 patients have performed at least one previous genetic test. Through PCR-free short-read WGS and a comprehensive in-house analytic pipeline, causative variants were found in 138 patients (138 of 322, 42.9%), 30 of which (30 of 138, 21.7%) are attributed to technically challenging variants. These included 6 variants in low-coverage regions with PCR bias, 2 deep intronic variants, 2 repeat expansions, 19 structural variants, and 2 variants in genes with a homologous pseudogene. The study demonstrated the indispensable diagnostic power of WGS in detecting technically challenging variants and the capability to serve as an all-in-one test for patients with high clinical suspicion of rare diseases.

PMID:40528347 | DOI:10.1016/j.xhgg.2025.100469

J Med Internet Res. 2025 Jun 18;27:e69929. doi: 10.2196/69929.

ABSTRACT

BACKGROUND: Diagnosing rare diseases remains challenging due to their inherent complexity and limited physician knowledge. Large language models (LLMs) offer new potential to enhance diagnostic workflows.

OBJECTIVE: This study aimed to evaluate the diagnostic accuracy of ChatGPT-4o and 4 open-source LLMs (qwen2.5:7b, Llama3.1:8b, qwen2.5:72b, and Llama3.1:70b) for rare diseases, assesses the language effect on diagnostic performance, and explore retrieval augmented generation (RAG) and chain-of-thought (CoT) reasoning.

METHODS: We extracted clinical manifestations of 121 rare diseases from China's inaugural rare disease catalog. ChatGPT-4o generated a primary and 5 differential diagnoses, while 4 LLMs were assessed in both English and Chinese contexts. The lowest-performing model underwent RAG and CoT re-evaluation. Diagnostic accuracy was compared via the McNemar test. A survey evaluated 11 clinicians' familiarity with rare diseases.

RESULTS: ChatGPT-4o demonstrated the highest diagnostic accuracy with 90.1%. Language effects varied across models: qwen2.5:7b showed comparable performance in Chinese (51.2%) and English (47.9%; χ²1=0.32, P=.57), whereas Llama3.1:8b exhibited significantly higher English accuracy (67.8% vs 31.4%; χ²1=40.20, P<.001). Among larger models, qwen2.5:72b maintained cross-lingual consistency considering the odds ratio (OR; Chinese: 82.6% vs English: 83.5%; OR 0.88, 95% CI 0.27-2.76,P=1.000), contrasting with Llama3.1:70b's language-dependent variation (Chinese: 80.2% vs English: 90.1%; OR 0.29,95% CI 0.08-0.83, P=.02). Cross-model comparisons revealed Llama3.1:8b underperformed qwen2.5:7b in Chinese (χ²1=13.22,P<.001) but surpassed it in English (χ²1=13.92,P<.001). No significant differences were observed between qwen2.5:72b and Llama3.1:70b (English: OR 0.33, P=.08; Chinese: OR 1.5, 95% CI 0.48-5.12,P=.07); qwen2.5:72b matched ChatGPT-4o's performance in both languages (English: OR 0.33, P=.08; Chinese: OR 0.44, P=.09); Llama3.1:70b mirrored ChatGPT-4o's English accuracy (OR 1, P=1.000) but lagged in Chinese (OR 0.33; P=.02). RAG implementation enhanced qwen2.5:7b's accuracy to 79.3% (χ²1=31.11, P<.001) with 85.9% retrieval precision. The distilled model Deepseek-R1:7b markedly underperformed (9.9% vs qwen2.5:7b; χ²1=42.19, P<.001). Clinician surveys revealed significant knowledge gaps in rare disease management.

CONCLUSIONS: ChatGPT-4o demonstrated superior diagnostic performance for rare diseases. While Llama3.1:8b demonstrates viability for localized deployment in resource-constrained English diagnostic workflows, Chinese applications require larger models to achieve comparable diagnostic accuracy. This urgency is heightened by the release of open-source models like DeepSeek-R1, which may see rapid adoption without thorough validation. Successful clinical implementation of LLMs requires 3 core elements: model parameterization, user language, and pretraining data. The integration of RAG significantly enhanced open-source LLM accuracy for rare disease diagnosis, although caution remains warranted for low-parameter reasoning models showing substantial performance limitations. We recommend hospital IT departments and policymakers prioritize language relevance in model selection and consider integrating RAG with curated knowledge bases to enhance diagnostic utility in constrained settings, while exercising caution with low-parameter models.

PMID:40532199 | DOI:10.2196/69929

HGG Adv. 2025 Jun 12;6(3):100468. doi: 10.1016/j.xhgg.2025.100468. Online ahead of print.

ABSTRACT

Incontinentia pigmenti (IP) is caused by loss-of-function variants in IKBKG, with molecular genetic diagnosis complicated by a pseudogene. We describe seven individuals from three families with IP but negative clinical genetic testing in whom long-read sequencing identified causal variants, including one family with the common exon 4-10 deletion not identified by conventional clinical genetic testing. Concurrent methylation analysis explained disease severity in one individual who died from neurologic complications, identified a mosaic variant in an individual with an atypical presentation, and confirmed skewed X chromosome inactivation in an XXY individual.

PMID:40515401 | DOI:10.1016/j.xhgg.2025.100468

Eur J Hum Genet. 2025 Jun 14. doi: 10.1038/s41431-025-01891-0. Online ahead of print.

ABSTRACT

Structural variants (SVs) of the nebulin gene (NEB), including intragenic duplications, deletions, and copy number variation of the triplicate region, are an established cause of recessively inherited nemaline myopathies and related neuromuscular disorders. Large deletions have been shown to cause dominantly inherited distal myopathies. Here we provide an overview of 35 families with muscle disorders caused by such SVs in NEB. Using custom Comparative Genomic Hybridization arrays, exome sequencing, short-read genome sequencing, custom Droplet Digital PCR, or Sanger sequencing, we identified pathogenic SVs in 35 families with NEB-related myopathies. In 23 families, recessive intragenic deletions and duplications or pathogenic gains of the triplicate region segregating with the disease in compound heterozygous form, together with a small variant in trans, were identified. In two families the SV was, however, homozygous. Eight of these families have not been described previously. In 12 families with a distal myopathy phenotype (of which 10 are previously unpublished), eight unique, large deletions encompassing 52-97 exons in either heterozygous (n = 10) or mosaic (n = 2) state were identified. In the families where inheritance was recessive, no correlation could be made between the types of variants and the severity of the disease. In contrast, all patients with large dominant deletions in NEB had milder, predominantly distal muscle weakness. For the first time, we establish a clear and statistically significant association between large NEB deletions and a form of distal myopathy. In addition, we provide the hitherto largest overview of the spectrum of SVs in NEB.

PMID:40517164 | DOI:10.1038/s41431-025-01891-0

Methods Mol Biol. 2025;2921:31-56. doi: 10.1007/978-1-0716-4502-4_2.

ABSTRACT

Despite the transformation of genomics and genetics, DNA- and RNA-based information provides only a partial view of disease etiology and pathogenesis. This has increased awareness that genetic and gene expression data must be integrated with downstream product activity and cellular metabolite regulation to understand disease processes fully.By simultaneously analyzing the genome, transcriptome, proteome, and metabolome, crucial molecular pathways and novel biomarkers associated with various genetic diseases have been identified using multi-omics approaches. A more comprehensive understanding of the complex interactions between genetic factors (genotype) and disease development (phenotype) has been enabled by these approaches.This chapter describes multi-omics protocols for genetic diseases, emphasizing metabolomics and proteomics approaches.

PMID:40515983 | DOI:10.1007/978-1-0716-4502-4_2

Front Endocrinol (Lausanne). 2025 May 29;16:1543991. doi: 10.3389/fendo.2025.1543991. eCollection 2025.

ABSTRACT

BACKGROUND: Satoyoshi syndrome (also known as Komuragaeri disease ) is a rare disorder of unknown etiology, with progressive muscle spasms, whole-body hair loss, and diarrhea as its main symptoms, particularly progressive skeletal muscle spasms and pain. Because of the lack of a clear etiology and pathogenesis of Satoyoshi syndrome, Western medicine lacks established effective therapies, and the long-term prognosis of the treatment of this disease is poor, unable to improve multiple symptoms simultaneously and prevent the recurrence of the disease. In recent years, acupuncture has been increasingly explored as a complementary treatment for autoimmune diseases. It is believed to exert its effects by modulating the neuroendocrine-immune network, enhancing immune cell function, and restoring homeostatic pathways. These mechanisms enable acupuncture to provide immune modulation, ultimately achieving a holistic and bidirectional regulatory effect.

CASE DESCRIPTION: We report the case of a 54-year-old male police officer who had Satoyoshi syndrome for more than five years. After six months of acupuncture treatment, the patient's chronic diarrhea completely disappeared, and the occasional painful muscle cramps and insomnia significantly improved. After six months of follow-up, the patient's condition was stable.

CONCLUSION: In this study, we believe that acupuncture therapy is of great significance for the improvement of diarrhea, immediate and long-term analgesia, and stabilization of the Satoyoshi syndrome.

PMID:40510477 | PMC:PMC12158676 | DOI:10.3389/fendo.2025.1543991

Int J Mol Sci. 2025 May 30;26(11):5297. doi: 10.3390/ijms26115297.

ABSTRACT

Single-cell transcriptomics data are analyzed using two popular tools, Seurat and Scanpy. Multiple separate tools are used downstream of Seurat and Scanpy cell annotation to study cell differentiation and communication, including cell proportion difference analysis between conditions, pseudotime and trajectory analyses to study cell transition, and cell-cell communication analysis. To automate the integrative cell differentiation and communication analyses of single-cell RNA-seq data, we developed a single-cell RNA-seq downstream analysis pipeline called "scDown". This R package includes cell proportion difference analysis, cell-cell communication analysis, pseudotime analysis, and RNA velocity analysis. Both Seurat and Scanpy annotated single-cell RNA-seq data are accepted in this pipeline. We applied scDown to a published dataset and identified a unique, previously undiscovered signature of neuronal inflammatory signaling associated with a rare genetic neurodevelopmental disorder. These findings were not identified with a simple implementation of Seurat differential gene expression analysis, illustrating the value of our pipeline in biological discovery. scDown can be broadly utilized in downstream analyses of scRNA-seq data, particularly in rare diseases.

PMID:40508102 | DOI:10.3390/ijms26115297

bioRxiv [Preprint]. 2025 May 31:2025.05.30.656844. doi: 10.1101/2025.05.30.656844.

ABSTRACT

Elucidating the role of somatic mutations in cancer, healthy tissues, and aging depends on methods that can accurately characterize somatic mosaicism across different cell types, as well as assay their impact on cellular function. Current technologies to study cell-type-specific somatic mutations within tissues are low-throughput. We developed Duplex-Multiome, incorporating duplex consensus sequencing to accurately identify somatic single-nucleotide variants (sSNV) from the same nucleus simultaneously analyzed for single-nucleus ATAC-seq (snATAC-seq) and RNA-seq (snRNA-seq). By introducing strand-tagging into the construction of snATAC-seq libraries, duplex sequencing reduces sequencing error by >10,000-fold while eliminating artifactual mutational signatures. When applied to 98%/2% mixed cell lines, Duplex-Multiome identified sSNVs present in 2% of cells with 92% precision and accurately captured known sSNV mutational spectra, while revealing unexpected subclonal lineages. Duplex-Multiome of > 51,400 nuclei from postmortem brain tissue captured sSNV burdens and spectra across all major brain cell types and subtypes, including those difficult to assay by single-cell whole-genome sequencing (scWGS). This revealed for the first time that diverse neuronal and glial cell types show distinct rates and patterns of age-related mutation, while also directly discovering developmental cell lineage relationships. Duplex-Multiome identified clonal sSNVs occurring at increased rates in glia of certain aged brains, as well as clonal sSNVs that correlated with changes in expression of nearby genes, in both neurotypical and autism spectrum disorder (ASD) individuals, directly demonstrating that somatic mutagenesis can contribute to gene expression phenotypes. Duplex-Multiome can be easily adopted into the 10X Multiome protocol and will bridge somatic mosaicism to a wide range of phenotypic readouts across cell types and tissues.

PMID:40502142 | PMC:PMC12154604 | DOI:10.1101/2025.05.30.656844

Orphanet J Rare Dis. 2025 Jun 12;20(1):303. doi: 10.1186/s13023-025-03818-w.

ABSTRACT

BACKGROUND: People with a rare disease find it difficult to obtain a diagnosis and access appropriate services. Evidence suggests that this can lead to health inequity amongst the rare disease community, i.e. systemic, unfair and avoidable differences in health opportunities and outcomes. This scoping review aims to identify and describe evidence on health inequities experienced by the rare disease community with regards to receipt of a diagnosis and access to health and social care services.

METHODS: We searched ASSIA, CINAHL, Embase, HMIC, MEDLINE and Social Policy and Practice for relevant studies. Studies were double screened at title and abstract and full-text using pre-specified inclusion criteria. As this research was commissioned by the UK National Institute for Health and Care Research Policy Research Programme, primary studies were limited to UK settings. These were supplemented with international systematic reviews. We also applied a 2010 date limit. Relevant data were extracted and presented narratively and tabulated.

RESULTS: One hundred thirty-six studies met the inclusion criteria, including 96 primary studies and 40 systematic reviews. The most frequently occurring rare diseases were motor neurone disease, cystic fibrosis and sickle cell disease. Seventeen types of inequity were identified: delayed diagnosis, lack of knowledge amongst clinicians, lack of information provision, limited services provision (across six different services), limited services for undiagnosed conditions, lack of care co-ordination; in addition, inequity was identified relating to place of residence, race/ethnicity, gender, socioeconomic status, age and disability.

CONCLUSION: This review has drawn attention to experiences of the rare disease community with respect to receipt of a diagnosis and access to services which are different to experiences in the general population, and within the rare disease community itself. Some of these experiences are clearly attributable to factors which are unfair, avoidable and systemic, particularly those which relate to specific groups in the rare disease community. Experiences relating to delayed diagnosis, lack of knowledge, information, care co-ordination and access to various services, also appeared to indicate inequity. These issues are less likely to be encountered with respect to more common diseases experienced in the general population.

PMID:40506782 | DOI:10.1186/s13023-025-03818-w

Pediatr Neurol. 2025 May 16;169:105-114. doi: 10.1016/j.pediatrneurol.2025.05.017. Online ahead of print.

ABSTRACT

BACKGROUND: SYNGAP1 is a heterogeneous genetic disorder associated with intellectual disability, infantile-onset seizures, and other neurological and somatic symptoms. Clinical trial design for SYNGAP1 would benefit from a disease concept model-i.e., enumerating and ranking symptoms based on their impact on affected individuals and their caregivers.

METHODS: We developed a disease concept model for SYNGAP1 via five exercises: a scoping review of clinical features, semistructured interviews with caregivers, a survey of caregivers, a survey of clinical experts, and a review of charts of individuals with SYNGAP1 at one center (Weill Cornell Medicine). We provide a narrative summary of the key findings.

RESULTS: We reviewed 19 articles, conducted 16 interviews with caregivers, received survey responses from 90 caregivers and 15 clinical experts, and reviewed seven charts. Integrating findings from these exercises indicates that both caregivers and providers consider seizures/epilepsy, intellectual disability, and emotional regulation to be the most important therapeutic targets. Caregivers also place a high priority on the ability of individuals with SYNGAP1 to communicate. Chart review revealed that some symptoms discussed in the caregiver interviews (i.e., lack of danger awareness, heat/cold intolerance, lack of satiety) are not found in clinicians' notes.

CONCLUSIONS: Seizures, intellectual disability, communication, and emotional regulation are the four most meaningful clinical outcomes to target for investigating clinical interventions for SYNGAP1, according to caregivers and providers.

PMID:40494056 | DOI:10.1016/j.pediatrneurol.2025.05.017